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December 1999 Conditional inference about generalized linear mixed models
Jiming Jiang
Ann. Statist. 27(6): 1974-2007 (December 1999). DOI: 10.1214/aos/1017939247

Abstract

We propose a method of inference for generalized linear mixed models (GLMM) that in many ways resembles the method of least squares. We also show that adequate inference about GLMM can be made based on the conditional likelihood on a subset of the random effects. One of the important features of our methods is that they rely on weak distributional assumptions about the random effects. The methods proposed are also computationally feasible. Asymptotic behavior of the estimates is investigated. In particular, consistency is proved under reasonable conditions.

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Jiming Jiang. "Conditional inference about generalized linear mixed models." Ann. Statist. 27 (6) 1974 - 2007, December 1999. https://doi.org/10.1214/aos/1017939247

Information

Published: December 1999
First available in Project Euclid: 4 April 2002

zbMATH: 0961.62062
MathSciNet: MR1765625
Digital Object Identifier: 10.1214/aos/1017939247

Subjects:
Primary: 62J12
Secondary: 62F12

Keywords: Asymptotic properties of estimates , maximum conditional likelihood , penalized generalized WLS , semiparametric inference

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.27 • No. 6 • December 1999
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